For an autoencoder anomaly detection system, model overfitting is characterized by a situation where all reconstructed inputs match the source inputs very closely, and therefore all reconstruction ...

For an autoencoder anomaly detection system, model overfitting is characterized by a situation where all reconstructed inputs match the source inputs very closely, and therefore all reconstruction ...

Second, we use a second autoencoder to enhance the data representation of the reconstructed rating matrix, which can alleviate the loss of some key feature information during reconstruction.

This paper proposes a learning-based approach for reconstruction of global illumination with very low sampling budgets (as low as 1 spp) at interactive rates. At 1 sample per pixel (spp), the Monte ...

We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations.

Detecting changes in asset co-movement using the autoencoder reconstruction ratio ARR aims to anticipate volatility patterns to provide signals for risk management and trading ...

Detecting changes in asset co-movements is of great importance to financial practitioners, with numerous risk management benefits arising from the timely detection of breakdowns in historical ...